This application relates generally to systems for targeting content to Internet users. More specifically, this application relates to systems that utilize social networks to target content to users.
The Internet makes an enormous amount of information available to users. For example, video, audio and text files abound, and users can participate in real time discussions on a whole range of topics. Individual users may find it difficult to find the information that is particularly useful or entertaining to them among the much greater amount of information that is not. Most popular lists may indicate information and sites that are popular with the overall group of Internet users, but may not be well suited for a particular user.
Search engines are useful, but require the user to filter the information, do not readily account for the taste of the user, do not show how the user is connected to a given search result and do not readily let users contact people who have either interacted with or created content inside a webpage. For instance, a user may search for travel information about Argentina on a web search engine and a search engine may require the user to filter through millions of results. Even a specific search such as “Buenos Aires hotels” returns a multitude of perspectives and no easy way to tell what information to trust. Further, the web contains many sources of disparate quality and thoroughness, including user generated content like blogs, user ratings/reviews and user comments. For instance, a user review may contain users discussing life in Buenos Aires, but one point about life in a particular neighborhood may not be detailed enough for a given user and the user wants more information. Websites offer disparate means of contacting users that don't reliably result in getting a satisfactory response so a user can't be sure to get a response after asking a question. Collectively users have a multitude of questions and answers about gaps in information on websites, but there is no ready way to add comments for other users to benefit. Owners and operators of businesses can masquerade as independent reviewers. When forced to trust a recommendation on a webpage, the user may make the wrong decision and have to face the impact without recourse. For instance, a user may choose to travel to a resort for vacation based on a review, but find changing resorts cost prohibitive or not feasible in case the recommended resort does not live up to expectations.
Traditionally, one way people have made decisions is to rely on the advice and experience of others when choosing to consume information, products, or services. In a bricks and mortar world, friends may provide recommendations on books, movies, music, magazines, and other sources of information and entertainment. Additionally, word of mouth recommendations are valuable when selecting goods or services. Word of mouth recommendations are particularly valuable because they come from people a user self identifies with and likely share some of the user's tastes.
Word of mouth recommendations are also very useful in the Internet context. Friends may recommend websites, movies, music, articles or other information that may be of interest to one another. However, such word of mouth recommendations, while valuable, are relatively cumbersome. To provide recommendations, users may send emails, text messages, or make phone calls to one another. Such modes of communication limit the ability for word of mouth recommendations to help target the vast amount of information on the Internet. Generally, the most valuable and useful information comes from within a person's own social network.
Social networks show the interrelation of acquaintances and may classify members by degrees of separation (i.e., the number of connections linking any two individuals in a social network). For example if individual A is connected to individual B through two intermediate acquaintances (C and D) they are connected at three degrees of separation.
When two people are connected by N number of intermediate acquaintances, the two are separated by N+1 degrees of separation. Computer implemented social networks, such as those described in U.S. Pat. No. 7,069,308, may be established to link those people who are within a maximum degree of separation (Nmax).
In their daily lives, people routinely operate within their social network to meet new people who exchange information for a variety of reasons such as sharing interests, contact information, education background and favorite quotes. Social networks enable sharing information by displaying a friend's activities about what updates friends have made to their information. For instance, a user may add a new photo or change a music interest to the band “Counting Crows” and another user may see the friend update. As such, social networks are useful for learning about what is happening inside the social network, but not for efficiently learning what is popular with users outside of the social network. Even for users willing to share their activities outside of the social network, there are many activities users simply do not want to share or would have reservations about sharing for privacy reasons. For instance, a user may not want to share that the user visited a webpage for a new movie with which the user would rather not publicly associate. Further, managing privacy for each and every website a user visits would be too time consuming. The privacy concerns are a barrier for users to be willing to share their web activities with others. Still, much of the content a user is interested in learning about or discovering comes from outside of a social network. For instance, a user may want to know that 19 of their friends watched a trailer for a new movie. There is a need for enabling users to share and discover content with other users in their social network without violating users' privacy.
There are several reasons people prefer meeting new people through social networks or relying on their social networks for information: it is more comfortable, it is more efficient, and it is more likely to lead to desirable relationships than other methods. Interacting through social networks is more comfortable than interacting with strangers. This is partly due to the fact that members of a social network may often have shared interests and tastes that effectively allow members to filter based on these traits by working within their social network.
Some methods of disseminating information in a social network have been investigated. For example, those methods and systems disclosed in U.S. Patent Publication No. 2008/0040673 filed by Zuckerberg, et al. relate to method of presenting information about members of a social network to other members of the same social network. This allows for the sharing of information regarding the members of the social network, such as the addition of new content or links created with new contacts, but, among other disparities, does not allow for the efficient sharing of information to members of the social network that is extraneous to the network based social network.
Other methods of providing recommendations to users have been proposed. For example, U.S. Pat. No. 6,853,982 to Smith, et al. discloses a system of recommending products to a user based upon those products already viewed by the user. Additionally, U.S. Pat. No. 7,113,917 to Jacobi, et al. disclose a method of recommending products to a user based upon the purchases of other users who have purchases in common with the user. However, among other disparities, neither of these methods provide for the advantages of word of mouth recommendations because the methods do not provide for linking users of a social network who are likely to share interests.
Accordingly, there is a need for a system of targeting content to Internet users that provides the benefits of word of mouth recommendations while removing the barriers created by cumbersome communications techniques.